Methods and apparatus to monitor work vehicles and to generate worklists to order the repair of such work vehicles should a machine failure be identified

ABSTRACT

Methods and apparatus to monitor work vehicles and to generate worklists to order the repair of such work vehicles should a machine failure be identified are disclosed. An apparatus includes an interface to access a first and second alerts from first and second work vehicle; an alert scorer to generate first and second scores for the respective first and second alerts; a machine health score determiner to determine first and second machine health score for the respective first and second work vehicles based on the first score and the first weighting factor and the second score and the second weighting factor; and a worklist generator to generate a worklist indicating that the second work vehicle is to be repaired prior to the first work vehicle based on at least one of the first and second machine health scores or an associated first and second classifications.

FIELD OF THE DISCLOSURE

This disclosure relates generally to work vehicles, and, moreparticularly, to methods and apparatus to monitor work vehicles and togenerate worklists to order the repair of such work vehicles should amachine failure be identified.

BACKGROUND

Routine maintenance may be performed on a work vehicle based on amaintenance schedule and/or alerts received. When such maintenance isperformed, diagnostic data may be obtained.

SUMMARY

An example apparatus includes an interface to access a first alert froma first work vehicle and a second alert from a second work vehicle; analert scorer to generate a first score for the first alert and a secondscore for the second alert, the first and second scores associated witha severity of the respective alerts; a weighting factor applier toassociate a first weighting factor with the first alert and to associatea second weighting factor with the second alert; a machine health scoredeterminer to determine a first machine health score for the first workvehicle based on the first score and the first weighting factor and asecond machine health score for the second work vehicle based on thesecond score and the second weighting factor; and a worklist generatorto generate a worklist indicating that the second work vehicle is to berepaired prior to the first work vehicle based on at least one of thefirst machine health score or an associated first classification and atleast one of the second machine health score or an associated secondclassification.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system for monitoring work vehicles toidentify possible machine failures and to generate worklists and workorders to repair such work vehicles should a machine failure beidentified.

FIG. 2 illustrates an example central data processing center that can beused to implement the system of FIG. 1.

FIG. 3 illustrates an example table including data generated and/or usedwhen implementing the examples disclosed herein.

FIG. 4 is an example graph generated using the examples disclosedherein.

FIG. 5 illustrates an example display including an example worklist torepair work vehicles generated in accordance with the teachings of thisdisclosure.

FIG. 6 illustrates an example display including example alerts andexample details of those alerts generated in accordance with theteachings of this disclosure.

FIG. 7 illustrates an example work order generated in accordance withthe teachings of this disclosure.

FIGS. 8, 9 and 10 are representative of machine readable instructionsthat may be executed to implement the example central data processingcenter of FIGS. 1 and/or 2.

FIG. 11 is a processor platform to execute the instructions of FIGS. 8,9 and 10 to implement the central data processing center of FIGS. 1and/or 2.

The figures are not to scale. Wherever possible, the same referencenumbers will be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

The examples disclosed herein relate to monitoring work vehicles toidentify potential machine failures and/or repairs and to schedule suchrepairs in an order that satisfies customer expectations. In otherwords, using the examples disclosed herein, resources for repairing workvehicles may be more efficiently allocated to increase customersatisfaction and/or to enable customers to be proactively supported. Bymonitoring the work vehicles and generating and comparing machine healthscores associated with such monitoring and/or other input insubstantially real time, those work vehicles requiring routinemaintenance may be identified and those work vehicles requiring moreurgent maintenance may be identified. The machine health score may bedetermined based on machine input, operator input, dealer input,manufacturer input, etc. However, the machine health score may bedetermined using different types of data and/or different sources ofdata. In some examples, the machine health score is a dynamic value thatis the result of continuous updates when the work vehicle is active andproviding diagnostic trouble codes (DTCs) and/or other data. In someexamples, machine health scores remain associated with the work vehiclewhen the machine health score satisfies a threshold and the machinehealth score and/or an associated alert has not be cleared and/orreviewed by a technician, etc. Additionally or alternatively, the latestmachine health score may overwrite and/or otherwise associated with thework vehicle.

The machine input may include alerts, measurement data, faults,calculations made to this data and/or diagnostic trouble codes (DTCs).The dealer input may include a weighting factor (e.g., a dealerimportance factor (DIF), a preference). The manufacturer input mayinclude a weighting factor and/or indicators that enable non-relevantalerts to be filtered. Some of the diagnostic trouble codes may beassigned a higher weight and/or urgency than others of the diagnostictrouble codes based on the associated severity. Thus, a first vehiclehaving a first diagnostic trouble code may be ranked ahead of a secondvehicle having a second diagnostic trouble code where the firstdiagnostic trouble code is assigned a higher weight and/or urgency thanthe second diagnostic trouble code. Thus, in some example, the machinehealth score can be determined based on the DTC, a combination of theDTCs, a frequency of occurrence of the respective DTCs and/or acombination of the DTCs and a weighting factor(s) (e.g., a dealerimportance factor (DIF)). While the weighting factor(s) may have anyvalue, in some examples, the weighting factor(s) is between zero and oneand/or changes seasonally based on the demand of the particular workvehicle and/or work vehicle types. In some examples, the weightingfactor and/or the DIF can be a binary (e.g., 0 or 1) multiplier. In someexamples, the weighting factor and/or the DIF can have a specific valuethat is between zero and one, inclusive, or within another selectedrange.

In some examples, the machine health score enables work vehicles withina fleet or otherwise to be categorized into different groups including afirst group of work vehicles, a second group of work vehicles and athird group of work vehicles. In some examples, the first group of workvehicles has a machine health score that satisfies a first thresholdindicating that those work vehicles require a first level urgency toservice or repair the work vehicles. The first level of urgency may beassigned a first color code (e.g., red). In some examples, the secondgroup of work vehicles has a machine health score that satisfies asecond threshold indicating that those work vehicles require a secondlevel urgency to service or repair the work vehicles. The second levelof urgency may be assigned a second color code (e.g., yellow). In someexamples, the third group of work vehicles has a machine health scorethat is less than the second threshold indicating that those workvehicles do not require service or repair. The third group of workvehicles may be assigned a third color code (e.g., blue). While theabove example mentions organizing the machine health scores and/or thework vehicles themselves into three groups, the machine health scoresand/or the work vehicles may be organized in any other suitable way.

While a value of the first threshold may have any correlation to a valueof the second threshold, in some examples, the first threshold is twostandard deviations from the second threshold. In some examples, thefirst and second thresholds are determined based on statistical analysisof one or more work vehicles and/or one or more fleets of work vehicles.For example, the thresholds may be determined by dividing the totalnumber of work vehicle event occurrences and/or measured samples intoequal groups and/or by mapping the work vehicle event occurrences into aprobability density function with or without skewing.

In some examples, the machine health scores (e.g., the machine healthdiagnostic scores) are used to determine a fleet score where the fleetscore is associated with a manufacturer, a model number(s), vehicletype(s), etc. The fleet scores may be organized and/or grouped intoquantiles such as, for example, four equal groups (e.g., quartiles),three equal groups, etc. Of course, the fleet scores may be grouped inany desired way including having one or more of the groups having moreor less entries than others of the groups. In other words, the number ofentries in each group may not be equal.

In examples in which there are three groups, a first one of the groupsmay be associated with urgent repair, a second one of the groups may beassociated with routine maintenance and a third one of the groups may beassociated with no action required. In some examples, the fleet scoresand/or other associated data may be provided via a communicationsnetwork as a service (e.g., a subscription service) to the owner of afleet of work vehicles and/or to a servicer (e.g., a dealer) who mayservice one or more fleets of vehicles. The fleet's scores and/or otherassociated data may be provided by the manufacturer and/or by any otherparty (e.g., a third party bureau and/or service provider).

In some examples, the alerts (DTCs) generated by the work vehicles areassociated with an event severity value and/or a dealer importancefactor (DIF). The event severity value and/or the dealer importancefactor may be changed based on input and/or preferences of themanufacturer, an entity (e.g., a dealer) servicing the work vehicleand/or an owner and/or operator of the work vehicle. In some examples,the dealer importance factor may be used to prioritize the order thatwork vehicles are serviced to increase customer satisfaction.Additionally or alternatively, in some examples, during a time of yearthat crops are being harvested (e.g., fall), the dealer importancefactor may prioritize servicing harvesters and/or combine equipment andduring a time of year that crops are being planted (e.g., spring), thedealer importance factor may prioritize servicing planters and/ortillage equipment. Additionally or alternatively, during a time of yearthat construction and/or road work is being completed (e.g., summer),the dealer importance factor may prioritize servicing constructionequipment and/or road making equipment.

The dealer importance factor may be changed based on user input receivedand/or may be automatically and/or dynamically updated. In examples inwhich the dealer importance factor is automatically and/or dynamicallyupdated, the dealer importance factor can be changed based on the timeof year, soil moisture conditions, weather conditions and/or a deadlineassociated with crop insurance. Of course, the dealer importance factormay be changed based on any number of other variables including thelocation of the work vehicle, the crop(s) being grown, the season, etc.

In some examples, an example display interface is used to displayinformation including a prioritized order to service work vehicles,alerts (e.g., diagnostic trouble codes), diagnoses, predictions and/oranalyses for the machine input received. Additionally or alternatively,in some examples, the display interface is used to display informationincluding copies of agreements in place (e.g., dealer serviceagreements), subscriptions associated with the work vehicles, the numberof hours that the work vehicles have been operated, warrantyinformation, machine location, etc. The work vehicles may be displayedin a work que format or a map or machine list format. Regardless of howthe worklist is displayed, identifiers (e.g., color coding) thatdifferentiate the prioritizing of the service or repair may be included.Of course, the work vehicles may be listed on the display interface inany other desirable way.

In some examples, based on the diagnoses and/or predictions (e.g., aprediction model), a work order/repair order is automatically generatedto service a work vehicle. In other examples, an individual can generatea work order after reviewing the alerts and/or the diagnosis for aparticular work vehicle. The diagnoses may include details of how torepair the work vehicle and/or the one or more parts and/or resources tobe allocated to repair the work vehicle. In examples in which thediagnoses includes an estimate of the one or more parts used to repairthe work vehicle, the examples disclosed herein may search an inventoryto determine the presence of the parts in the inventory. In someexamples, if a part to be used in a repair is not in inventory and/or isnot otherwise available, the examples disclosed herein automaticallyand/or otherwise generate an order for the part.

To enable the accuracy of the diagnosis and/or predictions to haveincreased accuracy, in some examples, the model is updateable. Forexample, the model may be updated based on feedback received on anaction taken by a service provider. Some feedback may be related toidentifying an alert or a sequence of alerts that are associated with aparticular repair and/or identifying an alert as not being relevant.

FIG. 1 is an example system 100 for generating a worklist thatprioritizes the repairs of first, second and third work vehicles 102,104, 106 based on different input received and/or a machine health scoredetermined. In this example, the input includes machine inputs 108, 110,112 from the work vehicles 102, 104, 106, owner/operator inputs 115,116, 118 from owner/operators 120, 122, 124 of the work vehicles 102,104, 106, service input 126 from a servicer (e.g., a dealer) 128 of thework vehicles 102, 104, 106 and manufacturer input 130 from amanufacturer 132 of the work vehicle 102, 104, 106.

In the illustrated example, the first work vehicle 102 is implemented bya backhoe loader, the second work vehicle 104 is implemented by a motorgrader and the third work vehicle 106 is implemented by a harvester.However, any of the work vehicles 102, 104, 106 may be implemented byany type of work vehicle. For example, one or more of the work vehicles102, 104, 106 may be implemented by any type of agricultural equipment,construction equipment, forestry equipment, lawn and/or turf equipment,etc. Further, while the example system 100 includes three work vehicles,in other examples, the system 100 may include any other number of workvehicles (e.g., 100 work vehicles; 1,000 work vehicles; 10,000 workvehicles, etc.) that are similarly and/or differently configured.

The machine input 108, 110, 112 may include alerts, measurement values,faults and/or diagnostic trouble codes (DTCs) that are communicated fromthe work vehicles 102, 104, 106 to an example central data processingcenter 113. In the illustrated example, the central data processingcenter 113 and/or any other components of FIG. 1 or otherwise disclosedherein may be coupled to an example network (e.g., the internet) 114. Insome examples, the alerts, measurement values, faults, DTCs areassociated with different repairs that have different urgencies,different resource allocations and/or are associated withrepairing/replacing different parts.

To determine which of the work vehicles 102, 104, 106 to prioritizerepairing, in some examples, the example central data processing center113 processes the machine inputs 108, 110, 112 by comparing the alertsand/or alert sequences within the machine inputs 108, 110, 112 toreference data and/or an associated model from an example referencedatabase 136. In some examples, the comparison enables alerts and/oralert sequences to be identified having an associated severity leveland/or classification, an associated repair/diagnosis predication and/oran associated probable parts prediction used to make such a repair. Insome examples, the reference data and/or the associated model includesmachine failures, alerts, measurement sequences, alert sequences and/orprobable parts used to repair the respective machine failures. In someexamples, the data within the model is linked, mapped and/or correlatedwithin the reference data and/or the associated model by the centraldata processing center 113. In some examples, when linking and/orcorrelating the data, the central data processing center 113 usesidentifiers to substantially ensure the accurate association of thedata.

If the central data processing center 113 determines there is a matchand/or a substantial match between one of the alerts and/or alertsequences contained within the machine inputs 108, 110, 112 and one ofthe reference alerts and/or reference alert sequences, in theillustrated example, the central data processing center 113 identifiesthe associated reference machine failure and generates work order data138 based on the same. The work order data 138 may include, for example,a work order, a probable list of parts used to repair one or more of thework vehicles 102, 104, 106 and/or directions and/or a manual to repairthe one or more work vehicles 102, 104, 106, etc. As used herein, asubstantial match between the alert sequence and one of the referencealert sequences means that the alert sequence of the machine input 108,110, 112 includes at least 90% of the alerts within the reference alertsequence (e.g., nine out of ten alerts of the alert sequence of themachine input 108, 110, 112 matches alerts of one of the reference alertsequences). In this example, the work order data 138 and/or anassociated notification can be provided to any individual and/or entity(e.g., business, dealership, fleet manager, etc.) associated with thework vehicles 102, 104, 106 including being provided to a work station140 at the servicer 128.

While the machine input 108, 110, 112 may be used to generate exampleworklist data 142 that details the order in which the work vehicles 102,104, 106 are to be repaired, in some examples, the example central dataprocessing center 113 also uses the owner/operator input 115, 116, 118,the servicer input 126 and/or the manufacture input 130 to generate theworklist 142. The owner/operator input 115, 116, 118, the servicer input126 and/or the manufacturer input 130 may include a weighting factor(s)and/or information identifying one or more alerts that may be classifiedas nuisance alerts. The owner/operator input 115, 116, 118, the servicerinput 126 and/or the manufacturer input 130 may be automatically and/ordynamically updated based on different factors. The factors may includethe time of year, soil conditions, crops being grown, the demand for thework vehicles 102, 104, 106, market fluctuations, pre-paid serviceplans, warranties, etc. In other words, the input 115, 116, 118, 126,130 and/or changes thereto enable the order in which jobs are displayedin the worklist data 142 to accurately reflect the current priorities ofthe owner/operators 120, 122, 124, the servicer 128 and/or themanufacturer 132.

In some such examples, to determine which of the work vehicles 102, 104,106 to prioritize repairing and, more generally, to generate theworklist 142, the example central data processing center 113 processesthe machine inputs 108, 110, 112 in combination with the owner/operatorinput 115, 116, 118, the servicer input 126 and/or the manufacturerinput 130. In some examples, the processing includes increasing theurgency of repairing one or more of the work vehicles 102, 104, 106 anddecreasing the urgency of repairing one or more of the work vehicles102, 104, 106, etc.

For example, the central data processing center 113 may generate a firstworklist in which the third work vehicle 106 is repaired first, thesecond work vehicle 104 is repaired second and the first work vehicle106 is repaired third and after processing the inputs 115, 116, 118, 126and/or 130, the central data processing center 113 may generate a secondworklist in which the first work vehicle 102 is repaired first, thesecond work vehicle 104 is repaired second and the third work vehicle106 is repaired third. In some examples, the reprioritizing of therepair of the first and third work vehicles 102, 106 is based on cropsbeing harvested and/or the owner/operator input 115, 116, 118, theservicer input 126 and/or the manufacturer input 130 assigning a higherpriority level to repairing the first work vehicle 102 as compared tothe third work vehicle 106 and/or the second work vehicle 104. While theabove example mentions updating a first worklist to a second worklist,the examples disclosed herein may continuously update the associatedmachine health scores and the worklists generated in accordance with theteachings of this disclosure based on the dynamically received data 108,110, 112, 115, 116, 118, 126, 130 in real-time or otherwise.

To enable the accuracy of the diagnosis and/or predictions to haveincreased accuracy, in some examples, the reference data and/or theassociated model at the reference database 136 is updateable. Forexample, the reference data and/or the associated model may be updatedbased on feedback received via one or more of the inputs 108, 110, 112,115, 116, 118, 126, 130. Thus, the example reference data and/or theassociated model may be updated in substantially real time based on dataassociated with the work vehicles 102, 104, 106 in communication withthe system 100. As set forth herein, “substantially real time” accountsfor transmission and/or processing delays.

FIG. 2 illustrates an example implementation of the example central dataprocessing center 113 of FIG. 2. In the illustrated example, the centraldata processing center 113 of FIG. 2 includes an example warrantydatabase 202, an example parts and associated maintenance database 204,an example reference alert and measurement database 206, an examplemodel generator 208, an example comparator 210, an example diagnosisidentifier 212, an example resources allocation determiner 214 and anexample probable parts determiner 216. In the illustrated example, thecentral data processing center 113 of FIG. 2 also includes a probableparts orderer 218, an example probable parts arrival estimator 220, anexample alert and measurement scorer 222, an example weighting factorapplier 224 and an example machine health score determiner 226. In theillustrated example, the example central data processing center 113 ofFIG. 2 also includes an example machine health score classifier 227, aworklist generator 228, an example work order generator 230, an exampleupdater 232, an example tallier 234, an example filter 236, an exampletime stamp identifier 238, an example interface 240 and an exampleweighting factor database 242. In some examples, the warranty database202, the parts and associated maintenance database 204, the referencealert and measurement database 206 and the weighting database 242 areimplemented at the reference database 136.

To generate a model used to identify potential machine failures,possible parts to repair such machine failures and, more generally, togenerate the work order data 138 and/or the worklist data 142, the modelgenerator 208 accesses the warranty data, the parts and associatedmaintenance data and/or the reference alert data from the respectivedatabases 202, 204, 206 and collates, links and/or otherwise associatesthe warranty data, the parts and associated maintenance data and/or thereference alert data to enable the data to be in a structured and/orqueryable format (e.g., a model, a framework, a structured model, astructured framework, etc.).

In some examples, generating the model includes the model generator 208classifying the alerts. For example, the alerts may be classified with afirst classification, a second classification and/or a thirdclassification. The first classification may be associated withparameters of the work vehicle 102, 104, 106 being in a normal operatingrange and/or within a threshold of the normal operating range (e.g., anormal range, a compliant range). The second classification may beassociated with the parameters of the work vehicle 102, 104, 106 beingoutside of the normal operating range and/or outside the threshold ofthe normal operating range (e.g., a transition range, a grey operatingrange). The third classification may be associated with the parametersof the work vehicle 102, 104, 106 being highly and/or significantlyoutside of the normal operating range and/or significantly outside thethreshold of the normal operating range (e.g., an abnormal range, anon-compliant range). In some examples, the first classification isassociated with a first color, the second classification is associatedwith a second color and the third classification is associated with athird color. Of course, any indicator may be used to differentiate theclassifications and the model generator 208 may use any number ofclassifications (e.g., 10 classifications, 100 classifications, 1000classifications).

The warranty data from the warranty database 202 may include maintenancelogs and/or failure information associated with work vehicles such as,for example, the first work vehicle 102, the second work vehicle 104and/or the third work vehicle 106. The parts and associated maintenancedata from the parts and associated maintenance database 204 may includesoftware information and/or part lists and/or technical documentationused when performing maintenance and/or repairs on work vehicles suchas, for example, the first work vehicle 102, the second work vehicle 104and/or the third work vehicle 106. In some examples, the softwareinformation includes updates and/or changes to software and/or processeson a work machine. The reference alert data from the reference alert andmeasurement database 206 may include machine alert and/or telematicsdata. Any one of the warranty database 202, the parts and associatedmaintenance database 204 and/or the reference alert and measurementdatabase 206 may be updatable based on the machine input 108, 110 and/or112, the owner/operator input 115, 116 and/or 118 and/or themanufacturer input 130.

To enable the classifications of the alerts to represent theprioritizations of the owner/operator 120, 122, 124, the servicer 128and/or the manufacturer 132, in the illustrated, the model generator 208accesses the weighting factor database 242 and/or one or more of theinputs 108, 110, 112, 115, 116, 118, 126, 130 to determine if any of thealerts within the model have an associated weighting factor. In examplesin which weighting factors are present for one or more of the alerts,the model generator 208 updates the model based on the weightingfactors, associates the alerts with the corresponding weighting factorand/or reclassifies the alert based on the weighting factor. While thisexample mentions updating the model and/or reclassifying alerts based onan associated weighting factor, these updates and/or reclassificationsmay be dealer specific, manufacturer specific, region specific, customerspecific and/or applicable to a work vehicle type and/or a particularmodel number.

In some examples, by associating a weighting factor with one or more ofthe alerts, the model generator 208 reclassifies the alert from a firstclassification to a second classification. Additionally oralternatively, in some examples, by associating a weighting factor withone or more of the alerts, the model generator 208 conditionallyclassifies a first alert as a fourth classification when the workvehicle 102, 104, 106 is a planter and conditionally classifies thefirst alert as a first classification when the work vehicle 102, 104,106 is a harvester. The weighting factors may be dynamically updated bythe owner/operator 120, 122, 124, the servicer 128 and/or themanufacturer 132. Thus, the classification of a particular alert for atype of work vehicle (e.g., a combine) and/or a group of work vehiclesmay be changed from one day to the next or from one season to the next.Furthermore, the classification of a particular alert for a type of workvehicle (e.g., a combine) and/or a group of work vehicles may be changedfrom one region and/or area to the next.

To enable the reference data and/or the model to incorporate feedbackfrom the owner/operator 120, 122, 124, the servicer 128 and/or themanufacturer 132, in the illustrated, the model generator 208 and/or theupdater 232 accesses one or more of the inputs 108, 110, 112, 115, 116,118, 126, 130 to determine if there is any feedback present for one ormore of the alerts within the model. In some examples, the feedbackincludes an indication that an alert and/or a sequence of alerts is anuisance alert and, thus, should be ignored and/or otherwise notaddressed. In some examples, the feedback includes an indication that analert and/or sequence of alerts is associated with a probable partslist, directions to repair the one or more work vehicles 102, 104, 106based on the alert, etc.

In the illustrated example, to identify machine failures within thealert data, the central data processing center 113 accesses the machineinput 108, 110, 112 at the interface 240 and the time stamp identifier238 identifies the time stamp associated with the respective alertsand/or identifies when the respective alerts were generated. In someexamples, the filter 236 filters alerts and/or alert sequences from themachine input 108, 110, 112 that were not generated within a thresholdamount of time. While any threshold of time may be selected, in someexamples, the filter 236 may filter alerts that were not generatedwithin 48-hours, 7-days, 14-days, 30-days, etc. In this example, as themachine input 108, 110, 112 is being accessed by the central dataprocessing center 113, the tallier 234 tallies the number of occurrencesof each alert from the different work machines 102, 104, 106.

To identify alerts within the machine input 108, 110, 112, thecomparator 210 compares the alerts accessed from the work vehicles 102,104, 106 to the model generated by the model generator 208 to determineif there is a substantial match between one or more of the alerts andthe reference alert data in the model. If there is a substantial matchbetween the alert from the work vehicles 102, 104, 106 and the referencedata, the diagnosis identifier 212 generates a diagnosis prediction torepair the work vehicle 102, 104, 106, the resources allocationdeterminer 214 determines the resources to be allocated to repair thework vehicle 102, 104, 106 and/or the probable parts determiner 216determines the probable parts used to repair the work vehicle 102, 104,106.

In examples in which the probable parts determiner 216 determines thatparts are to be used to repair of the work vehicle 102, 104, 106, theprobable parts orderer 218 determines whether the part is in stockand/or is otherwise available. In examples in which the probable partsorderer 218 determines that the part and/or parts is not in stock, theprobable parts orderer 218 places an order for the part and the probableparts arrival estimator 220 estimates when the part will arrive. In someexamples, the determinations made by one or more of the diagnosisidentifier 212, the resources allocation determiner 214, the probableparts determiner 216, the probable parts orderer 218 and/or the probableparts arrival estimator 220 may be used to determine if and/or when arepair(s) is to be performed on the work vehicle 102, 104, 106.

To determine the severity of the alert, in the illustrated example, thealert and measurement scorer 222 references the reference data and/orthe model to determine if there is a classification within the model forthe alerts within the machine inputs 108, 110, 112. In some examples,the alert and measurement scorer 222 determines if there is asubstantial match between the machine input 108, 110, 112 and thereference alert having a corresponding classification based on acomparison. In examples in which there is a substantial match betweenthe machine input 108, 110, 112 and the reference alert having acorresponding classification, the alert and measurement scorer 222associates the classification with the respective alerts.

To prioritize the repair of the work vehicles 102, 104, 106 based on theinput 108, 110, 112, 115, 116, 118, 126 and/or 130 and/or the weightingfactor database 242, in the illustrated example, the weighting factorapplier 224 determines if there is a weighting factor for one or more ofthe alerts within the reference data. In examples in which there is aweighting factor associated with the alert, the weighting factor applier224 applies the weighting factor to the associated alert, reclassifiesthe alert based on the weighting factor and/or otherwise associates thealert with the corresponding weighting factor.

To generate a machine health score for the different work vehicles 102,104, 106, in the illustrated example, the machine health scoredeterminer 226 accesses the data from one or more of the tallier 234,the diagnosis identifier 212, the resources allocation determiner 214,the probable parts determiner 216, the probable parts orderer 218, theprobable parts arrival estimator 220, the alert and measurement scorer222, the weighting factor applier 224 and, more generally, the inputs108, 110, 112, 115, 116, 118, 126, 130 from the different sources 102,104, 106, 120, 122, 124, 128, 132. In some examples, the determinedmachine health scores are classified by the machine health scoreclassifier 227. For example, some of the machine health scores may beclassified in a manner that indicates that the work machine 102, 104,106 needs attention, the work machine 102, 104, 106 is to be watchedand/or monitored and/or the work machine 102, 104, 106 is operatingnormally. In some examples, the machine health scores are grouped byidentifying a first threshold above an average machine health score anda second threshold below the average machine health score.

Equation 1 below provides an example of how the machine health scoresmay be determined. Referring to Equation 1, DateTimeRatio represents thetime period that is being examined, ΣMHMValue_(DTC) represents thesummation of machine health scores for each DTC,ΣMHMValue_(partAlertValue) represents the summation of the machinehealth scores for each alert value, ΣMHMValue_(MaintenanceAlert)represents the summation of the machine health scores for eachmaintenance alert value and ΣMHM_(Total) represents the summation of themachine health scores including the machine health scores for each DTC,the machine health scores for each alert value and the machine healthscore for each maintenance value.

MHMValue_(Total)=[DateTimeRatio*(ΣMHMValue_(DTC))]+[ΣMHMValue_(Part Alert Value)]+[ΣMHMValue_(Maintenance Alert)]  Equation1:

In some examples, the value of DateTimeRatio is larger when the timeperiod is smaller

$( {{e.g.},{\frac{24\mspace{14mu} {hours}}{12\mspace{14mu} {hours}} = 2}} )$

and the value of the DateTimeRatio is smaller when the time period islarger

$( {{e.g.},{\frac{24\mspace{14mu} {hours}}{168\mspace{14mu} {hours}} = 0.143}} ).$

In some examples, Equation 2 is used to determine the MHM Value_(DTC).

MHMValue_(DTC)=Severity score*Occurance count score*Visibilityscore*Dealer importance factor  Equation 2:

Referring to Equation 2, the severity score may be associated with afirst symbol (e.g., a stop symbol) indicating that the work vehicle hasstopped working, a second symbol (e.g., a warning symbol) indicating aservice alert, a third symbol (e.g., an information symbol) indicatingan information alert or a grey alert indicating that data is missing. Insome examples, when a count score of a particular alert occurring isgreater than a first value, the occurrence count score is a firstoccurrence count score, when the count score is less than the firstvalue and greater than a second value, the occurrence count score is asecond occurrence count score and when the count score is less than thesecond value, the occurrence count score is a third occurrence countscore. In some examples, the visibility score is associated with a firstvalue when the alert and/or issue is visible and/or noticeable to theoperator of the work vehicle and the visibility score is associated witha second value when the alert and/or issue is not visible and/or notnoticeable to the operator of the work vehicle. In some examples, thedealer importance factor is associated with a platform and enables adiagnostic trouble code (DTC) to change based on insights on the DTC, atime of year, etc. In other words, the dealer importance factor enablesthe DTC to be weighted based on one or more preferences. Becauseexamples disclosed herein may receive numerous MHM Value_(Dtc), the MHMValue_(Dtcs) may be summed as shown in Equation 1.

In some examples, the MHM Value_(Part Alert Value) is associated with afirst value and/or a first color (e.g., red) when the alert is critical(e.g., the machine failure is imminent). In some examples, the MHMValue_(Part Alert Value) is associated with a second value and/or asecond color (e.g., yellow) when the alert is major and/or when themachine failure will likely occur but it not imminent. In some examples,the MHM Value_(Part Alert Value) is associated with a third value and/ora third color (e.g., blue) when the alert is normal and/or when themachine is operating normally. Because the examples disclosed herein mayreceive numerous MHM Value_(Part Alert Values), the MHMValue_(part Alert Values) may be summed as shown in Equation 1.

In some examples, the MHM Value_(Maintenance Alert) changes from a lowvalue and/or a first color (e.g., blue) to a higher value and/or asecond color (e.g., yellow) as the work machine gets closer to a timefor service. Because the examples disclosed herein may receive numerousMHM Value_(Maintenance Alerts), the MHM Value_(Maintenance Alerts) maybe summed as shown in Equation 1.

In some examples, the determined machine health scores and/or theassociated classifications are compared by the comparator 210 toreference data and/or otherwise to determine which of the work vehicles102, 104, 106 are to be repaired and/or the order in which one or moreof the work vehicles 102, 104, 106 are to be repaired. One of the workvehicles 102, 104, 106 having a higher machine health score and/orassociated classification may cause that work vehicle 102, 104, 106 tobe scheduled ahead of the other work vehicles 102, 104, 106 having alower machine health score and/or associated classification.

Referring to FIG. 3, an example chart 300 is included of example MHMValue_(DTCs) 302, example MHM Value_(Part Alert Values) 304, exampleMHMValue_(Totals) 306 and example DateTimeRatios 308 for an examplefirst scenario 310, an example second scenario 312 and an example thirdscenario 314. In the example chart 300, the values 302, 304, 306 areprovided for different divisions 316 of work machines 318.

FIG. 4 illustrates an example graph 400 including a y-axis 402representing ranks and/or machine health scores for a time period (e.g.,one day) and an x-axis 404 representing the date and/or the time period.In the illustrated example, each of data entries 406 represent a machinehealth score for a single day for different ones of the work vehicles.Based on processing the data entries 406, a first threshold and/orbreakpoint 408 is determined based on identifying a first group 410 ofthe data entries 406 below an average of the machine health scores ofthe data entries 406. Additionally, based on processing the data entries406, a second threshold and/or breakpoint 412 is determined based onidentifying a second group 414 of the data entries 406 above the averageof the machine health scores represented by the data entries 406.Additionally, based on processing the data entries 406, a third group416 of the data entries 406 is determined between the first and secondthresholds 408, 412 associated with the average. In some examples, thefirst group 410 is associated with a first and/or blue rank indicatingthat the work machine is operating normally. In some examples, thesecond group 414 is associated with a third and/or red rank indicatingthat the work machine is to be serviced and/or needs attention. In someexamples, the third group 416 is associated with a second and/or yellowrank indicating that the work machine is to be watched. In someexamples, the breakpoints 408, 412 can be determined using historic DTCdata and/or measurement data to determine what rank (e.g., a first rank,a second rank, etc.) and/or what color code (e.g., a first color code, asecond color code, etc.) a newly received DTC is associated with.

Referring back to FIG. 2, in examples in which the machine health scoredeterminer 226, the machine health score classifier 227 and/or thecomparator 210 determines that one or more repairs are to be performedon the first work vehicle 102, the work order generator 230 generatesthe work order data 138 associated with performing a repair on the firstwork vehicle 102. In examples in which the machine health scoredeterminer 226, the machine health score classifier 227 and/or thecomparator 210 determines that one or more repairs are to be performedon the first work vehicle 102 and the second work vehicle 102 and norepairs are to be performed on the third work vehicle 106, the worklistgenerator 228 may generate the worklist data 142 that prioritizes therepair of one of the work vehicles 102, 104 over the other of the workvehicles 102, 104. In some examples, the repair of one of the workvehicles 102, 104, 106 over others of the work vehicles 102, 104, 106may be based on the time of year, an agreement in place between thesecond owner/operator 122 and the servicer 128 and/or any other reason.

While an example manner of implementing the central data processingcenter 113 of FIG. 1 is illustrated in FIG. 2, one or more of theelements, processes and/or devices illustrated in FIG. 2 may becombined, divided, re-arranged, omitted, eliminated and/or implementedin any other way. Further, the example warranty database 202, theexample parts and associated maintenance database 204, the examplereference alert and measurement database 206, the example modelgenerator 208, the example comparator 210, the example diagnosisidentifier 212, the example resources allocation determiner 214, theexample probable parts determiner 216, the probable parts orderer 218,the example probable parts arrival estimator 220, the example alert andmeasurement scorer 222, the example weighting factor applier 224, theexample machine health score determiner 226, the example machine healthscore classifier 227, the example worklist generator 228, the examplework order generator 230, the example updater 232, the example tallier234, the example filter 236, the example time stamp identifier 238, theexample interface 240, the example weighting factor database 242 and/or,more generally, the example central data processing center 113 of FIG. 1may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the example warranty database 202, the example parts andassociated maintenance database 204, the example reference alert andmeasurement database 206, the example model generator 208, the examplecomparator 210, the example diagnosis identifier 212, the exampleresources allocation determiner 214, the example probable partsdeterminer 216, the probable parts orderer 218, the example probableparts arrival estimator 220, the example alert and measurement scorer222, the example weighting factor applier 224, the example machinehealth score determiner 226, the example machine health score classifier227, the example worklist generator 228, the example work ordergenerator 230, the example updater 232, the example tallier 234, theexample filter 236, the example time stamp identifier 238, the exampleinterface 240, the example weighting factor database 242 and/or, moregenerally, the example central data processing center 113 of FIG. 1could be implemented by one or more analog or digital circuit(s), logiccircuits, programmable processor(s), application specific integratedcircuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example warrantydatabase 202, the example parts and associated maintenance database 204,the example reference alert and measurement database 206, the examplemodel generator 208, the example comparator 210, the example diagnosisidentifier 212, the example resources allocation determiner 214, theexample probable parts determiner 216, the probable parts orderer 218,the example probable parts arrival estimator 220, the example alert andmeasurement scorer 222, the example weighting factor applier 224, theexample machine health score determiner 226, the example machine healthscore classifier 227, the example worklist generator 228, the examplework order generator 230, the example updater 232, the example tallier234, the example filter 236, the example time stamp identifier 238, theexample interface 240, the example weighting factor database 242 and/or,more generally, the example central data processing center 113 of FIG. 1is/are hereby expressly defined to include a non-transitory computerreadable storage device or storage disk such as a memory, a digitalversatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc.including the software and/or firmware. Further still, the examplecentral data processing center 113 of FIG. 1 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 2, and/or may include more than one of any or all ofthe illustrated elements, processes and devices.

FIG. 5 illustrates an example display 350 generated in accordance withthe teachings of this disclosure that may be displayable at the workstation 140 and/or at any other location. In the illustrated example,the display 350 includes a worklist 352 including entries displayed incolumns 354, 356, 358, 360, 362 and corresponding rows 364, 366, 368,370, 372, 374.

In this example, the first column 354 describes different work vehiclesin queue to be repaired, the second column 356 displays a tally of thealerts generated by the different work vehicles and the third column 358displays a tally of the agreements associated with the different workvehicles. Additionally, in this example, the fourth column 360 displaysa tally of the jobs associated with the different work vehicles and thefifth column 362 displays the number of hours that the different workvehicles have been used.

In some examples, the agreements are service agreements between theowner/operator of the work vehicles and the servicer that services thework vehicles (e.g., the dealer). In some examples, the agreements areaccessible and/or viewable by selecting and/or otherwise hovering overan agreement icon in one or more of the rows 364, 366, 368, 370, 372,374. In some examples, the jobs are associated with work orders thatdescribe services to be performed on the work vehicles that are listedin the different rows 364, 366, 368, 370, 372, 374. In some examples,the jobs and/or the work orders are accessible and/or viewable byselecting and/or otherwise hovering over a job icon in one or more ofthe rows 364, 366, 368, 370, 372, 374.

In the illustrated example, the display 350 also includes a machine tab376, a model tab 378, a job tab 380 and a customer tab 382. In someexamples, the tabs 376, 378, 380, 382 are selectable to, for example,group the entries from the different rows 364, 366, 368, 370, 372, 374in different ways. For example, if the customer tab 382 is selected,worklist entries for the same customer may be grouped together. If themodel tab 378 is selected, worklist entries for the same model may begrouped. Further, the example display 350 includes search bars 384, 386,388 to enable a user to enter criteria to search the worklist 352 basedon, for example, a customer, an agreement, a job, a model, a serialnumber, a location, a product, etc.

In this example, the display 350 includes a first expandable entry 390relating to jobs, a second expandable entry 392 relating to agreements,a third expandable entry 394 relating to support plans and a fourthexpandable entry 396 relating to subscriptions. As shown in the exampleof FIG. 3, the first expandable entry 390 is expanded to display bothhigh-level details relating to jobs (e.g., 2268 active jobs) andmore-focused details on at least some of those jobs (e.g., 6high-priority jobs).

FIG. 6 illustrates an example display 450 that can be generated inresponse to one of the alert tallies in the second column 356 of FIG. 5being selected. In some examples, the display 450 is displayable at thework station 140 of FIG. 1 and/or at any other location. In theillustrated example, the display 450 includes a color-codedprioritization indicator 451, a first expandable entry 452 relating topriority diagnostic trouble codes (DTCs), a second expandable entry 454relating to DTCs that are already on jobs and/or being addressed and athird expandable entry 456 relating to nuisance and/or DTCs that can beignored. As shown in the example of FIG. 6, the color-codedprioritization indicator 451 is a vertical bar along a side of thedisplay 450 having a color (e.g., yellow). In some examples, thecolor-coded prioritization indicator 451 indicates that a work vehicle458 being serviced by a service provider 459 is outside of the normaloperating range. Of course, the color-coded prioritization indicator 451can be any color to provide an indication of the priority of the alert,the repair of the work vehicle 458, etc.

In this example, the first expandable entry 452 is shown expanded todisplay both high-level details relating to the priority DTCs (e.g., 2priority DTCs) and more-focused details relating to the alerts in rows460, 462, 464, 466. For example, the rows 460, 462, 464, 466 includedetails on the number of times a particular alert has been generated andcolor-coded indicators including a first color code 468 and a secondcolor code 470. Additionally, in this example, the rows 460, 462, 464,466 include a description of the alert and notes and/or suggestions onaddressing the alert and/or repairing the work vehicle 458. In someexamples, the first color code 468 is indicative of the alert having asecond level of urgency and the second color code 470 is indicative ofthe alert having a third level of urgency. Of course, the color codes468, 470 can have any other type of meaning.

Further, the example display 450 includes a first icon 472, a secondicon 474, a third icon 476, an operating hours indication 478, awarranty presence indication 480, a machine location indicator 482 and alast call-in description 484. In some examples, selecting the first icon472 enables one or more of the alerts to be identified as resolvedand/or “all good” and such feedback may be incorporated into one or moreof the inputs 108, 110, 112, 115, 116, 118, 126, 130. In some examples,selecting the second icon 474 reports a nuisance alert and/or an alertthat can be ignored and such feedback may be incorporated into one ormore of the inputs 108, 110, 112, 115, 116, 118, 126, 130. In someexamples, selecting the third icon 476 enables a new work order and/orjob to be created. In some examples, reporting the nuisance alert isincluded in the servicer input 126 and the new work order is included inthe work order data 138 and/or the worklist data 142.

FIG. 7 illustrates an example display 500 generated in response to thesecond icon 474 being selected that may be displayable at the workstation 140 of FIG. 1 and/or at any other location. In some examples,the work orders are automatically generated. In some examples, the workorders are generated based on input received from a technician, anoperator and/or another individual associated with the work vehicle.

In the illustrated example, the display 500 includes a dropdown menu501, a first field 502 including details on the job, a second field 504including details on a job code, a third field 506 including details onthe location of the work vehicle 458 and/or a location of the serviceprovider and a fourth field 508 including details on the department toperform the diagnosis and/or the repair of the work vehicle 458. In thisexample, the dropdown menu 501 is showing that “actions” have beenselected enabling an action section 510 to be displayed. Additionally,in the illustrated example, the display 500 includes a fifth field 512including details on when the work order was created, a sixth field 514including details of the complaint, alert and/or problem being faced bythe work vehicle 458 and a seventh field 516 including a description onhow to address the complaint, alert and/or problem including, forexample, reference materials.

A flowchart representative of example machine readable instructions forimplementing the central data processing center 113 of FIG. 2 is shownin FIGS. 8, 9 and 10. In this example, the machine readable instructionscomprise a program for execution by a processor such as the processor912 shown in the example processor platform 900 discussed below inconnection with FIG. 11. The program may be embodied in software storedon a non-transitory computer readable storage medium such as a CD-ROM, afloppy disk, a hard drive, a digital versatile disk (DVD), a Blu-raydisk, or a memory associated with the processor 912, but the entireprogram and/or parts thereof could alternatively be executed by a deviceother than the processor 912 and/or embodied in firmware or dedicatedhardware. Further, although the example program is described withreference to the flowchart illustrated in FIGS. 8, 9 and 10, many othermethods of implementing the example central data processing center 113may alternatively be used. For example, the order of execution of theblocks may be changed, and/or some of the blocks described may bechanged, eliminated, or combined. Additionally or alternatively, any orall of the blocks may be implemented by one or more hardware circuits(e.g., discrete and/or integrated analog and/or digital circuitry, aField Programmable Gate Array (FPGA), an Application Specific Integratedcircuit (ASIC), a comparator, an operational-amplifier (op-amp), a logiccircuit, etc.) structured to perform the corresponding operation withoutexecuting software or firmware.

As mentioned above, the example processes of FIGS. 8, 9 and 10 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim lists anythingfollowing any form of “include” or “comprise” (e.g., comprises,includes, comprising, including, etc.), it is to be understood thatadditional elements, terms, etc. may be present without falling outsidethe scope of the corresponding claim. As used herein, when the phrase“at least” is used as the transition term in a preamble of a claim, itis open-ended in the same manner as the term “comprising” and“including” are open ended.

The program of FIG. 8 begins at block 602 with the model generator 208generating a model by accessing the warranty data, the parts andassociated maintenance data and/or the reference alert data from therespective databases 202, 204, 206 and collating, linking and/orotherwise associating the warranty data, the parts and associatedmaintenance data and/or the reference alert data to enable the data tobe in a structured and/or queryable format (block 602). The central dataprocessing center 113 accesses the machine input 108, 110, 112 at theinterface 240 from the work vehicle 102, 104, 106 (block 604). Thecentral data processing center 113 processes the alert data (block 606).

To generate a machine health score for the different work vehicles 102,104, 106, in the illustrated example, the machine health scoredeterminer 226 accesses the processed data and generates a machinehealth score for each of the work vehicles 102, 104, 106 and/or themachine health score classifier 227 determines an associatedclassification for the machine health scores determined (block 608). Insome examples, accessing the processed data includes accessing data fromone or more of the tallier 234, the diagnosis identifier 212, theresources allocation determiner 214, the probable parts determiner 216,the probable parts orderer 218, the probable parts arrival estimator220, the alert and measurement scorer 222, the weighting factor applier224 and, more generally, one or more of the inputs 108, 110, 112, 115,116, 118, 126, 130, and generating a machine health score for each ofthe work vehicles 102, 104, 106 based on processing the accessed data.The comparator 210 compares the determined machine health scores and/orthe associated classifications to reference data and/or otherwise todetermine which of the work vehicles 102, 104, 106 are to be repairedand, if the work vehicles 102, 104, 106 are to be repaired, the order inwhich of the work vehicles 102, 104, 106 are to be repaired (block 610).

In examples in which the machine health score determiner 226, themachine health score classifier 227 and/or the comparator 210 determinesthat one or more repairs are to be performed on one or more of the workvehicles 102, 104, 106, the work order generator 230 generates the workorder data 138 that causes a repair to be initiated on the one or morework vehicles 102, 104, 106 (block 612). In examples in which themachine health score determiner 226, the machine health score classifier227 and/or the comparator 210 determines that one or more repairs are tobe performed on the work vehicles 102, 104, 106, the worklist generator228 generates the worklist data 142 that prioritizes the repair of oneof the work vehicles 102, 104, 106 over the other of the work vehicles102, 104 (block 614). At block 616, the central data processing center113 provides the servicer 128 access to the work order data 138 and/orthe worklist data 142 for display at, for example, the work station 140(block 616).

FIG. 9 illustrates an example of implementing block 602 of FIG. 8. Theprogram of FIG. 9 begins at block 702 with the model generator 208accessing the warranty data, the parts and associated maintenance dataand/or the reference alert and measurement data from the respectivedatabases 202, 204, 206 (block 702). The model generator 208 collates,links and/or otherwise associates the warranty data, the parts andassociated maintenance data and/or the reference alert data to enablethe data to be in a structured and/or queryable format (e.g., a model, aframework, a structured model, a structured framework, etc.) (block704). The model generator 208 associates the reference alerts withcorresponding classifications such as a first classification and/orurgency of repair, a second classification and/or urgency of repairand/or a third classification and/or urgency of repair (block 706).

To enable the classifications of the alerts to represent theprioritizations of the owner/operator 120, 122, 124, the servicer 128and/or the manufacturer 132, in the illustrated example, the modelgenerator 208 accesses the weighting factor database 242 and/or theowner/operator input 115, 116, 118, the work order data 138 and/or theworklist data 142 to determine if any weighting factors are present forone or more of the alerts within the model (block 708). In examples inwhich weighting factors are present for one or more of the alerts, themodel generator 208 updates the model based on the weighting factorsand, more generally, associates the one or more alerts with thecorresponding weighting factor(s) (block 710).

To enable the model and/or the alerts to incorporate feedback from theowner/operator 120, 122, 124, the servicer 128 and/or the manufacturer132, in the illustrated example, the model generator 208 and/or theupdater 232 accesses the owner/operator input 115, 116, 118, the workorder data 138 and/or the worklist data 142 to determine if any feedbackis present for one or more of the alerts within the model and/or for oneor more of the work vehicles 102, 104, 106, etc. (block 712). Inexamples in which feedback is present, the model generator 208 and/orthe updater updates the model based on the feedback and/or other inputreceived (block 714). The process then returns to FIG. 8.

FIG. 10 illustrates an example of implementing block 606 of FIG. 8. Theprogram of FIG. 10 begins at block 802 with the central data processingcenter 113 accessing and/or selecting one of the alerts of the machineinput 108, 110, 112 at the interface 240 from the work vehicle 102, 104,106 (block 802) and the time stamp identifier 238 determining when theselected alert was generated (block 804). The filter 236 processes thealerts within the machine input 108, 110, 112 and determines whether thealerts were generated within a threshold amount of time based on thetime stamp identified (block 806). Based on the processing, the filter236 filters the alerts from the machine inputs 108, 110, 112 that wereidentified as not being generated within the threshold amount of time(block 808). At block 810, the central data processing center 113determines if there are additional alerts available from one or more ofthe work vehicles 102, 104, 106 (block 810). In examples in whichadditional alerts are available, the central data processing center 113accesses a subsequent alert from the alert data of the machine input108, 110, 112 at the interface 240 (block 812).

However, in examples in which additional alerts are not available, thetallier 234 determines the number of occurrences of each alert type fromthe work vehicles 102, 104, 106 (block 814) and the comparator 210compares the alerts accessed from the work vehicles 102, 104, 106 toreference data and/or alerts within the model (block 816). At block 818,the comparator 210 determines if there is a substantial match betweenone or more of the alerts and the reference alert data in the model(block 818). If there is a substantial match between one or more of thealerts and the reference data, the diagnosis identifier 212 determines adiagnosis associated with the repair (block 820). Further, if there is asubstantial match between one or more of the alerts and the referencedata, the resources allocation determiner 214 determines the resourcesto be allocated to repair the associated work vehicle 102, 104, 106and/or estimates the amount of time to repair the associated workvehicles 102, 104, 106 (block 822). Further, if there is a substantialmatch between one or more of the alerts and the reference data, theprobable parts determiner 216 determines the probable parts used toperform the repair on the work vehicle 102, 104, 106 (block 824). Atblock 826, the central data processing center 113 associates theprobable diagnosis, the estimated resources and/or time and/or theprobable parts list with the corresponding alert (block 826).

In examples in which the probable part determiner 216 determines thatparts are to be used to repair the work vehicle 102, 104, 106, theprobable parts orderer 218 determines whether the part and/or parts tobe used during the repair are in stock and/or are otherwise available(block 828). In examples in which the probable parts orderer 218determines that the part and/or parts are not in stock, the probableparts orderer 218 places an order for the one or more parts (block 830)and the probable parts arrival estimator 220 estimates when the partswill arrival (block 832). At block 834, the central data processingcenter 113 associates the estimated arrival time of the probable partswith the corresponding alert (block 834).

To determine the severity of and/or to otherwise classify the alerts,the alert and measurement scorer 222 determines if there is aclassification for one or more of the alerts within the model (block836). In examples in which there is a substantial match between the oneor more alerts from the machine input 108, 110, 112 and a referencealert having a corresponding classification, the alert and measurementscorer 222 associates the classification with the corresponding alertfrom the machine input 108, 110, 112 (block 838).

The weighting factor applier 224 determines if there is a weightingfactor for one or more of the alerts from the machine input 108, 110,112 using, for example, data from the model and/or data from theweighting factor database 242 (block 840). In examples in which there isa weighting factor associated with the alert from the machine input 108,110, 112, the weighting factor applier 224 applies the weighting factorto the associated alert and/or otherwise associates the alert with thecorresponding weighting factor (block 842). The process then returns toFIG. 8.

FIG. 11 is a block diagram of an example processor platform 900 capableof executing the instructions of FIGS. 8, 9 and 10 to implement thecentral data processing center 113 of FIG. 2. The processor platform 900can be, for example, a server, a personal computer, a mobile device(e.g., a cell phone, a smart phone, a tablet such as an iPad™), apersonal digital assistant (PDA), an Internet appliance, or any othertype of computing device.

The processor platform 900 of the illustrated example includes aprocessor 912. The processor 912 of the illustrated example is hardware.For example, the processor 912 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors or controllers fromany desired family or manufacturer. The hardware processor may be asemiconductor based (e.g., silicon based) device. In this example, theprocessor 912 implements the example model generator 208, the examplecomparator 210, the example diagnosis identifier 212, the exampleresources allocation determiner 214, the example probable partsdeterminer 216, the probable parts orderer 218, the example probableparts arrival estimator 220, the example alert and measurement scorer222, the example weighting factor applier 224, the example machinehealth score determiner 226, the example machine health score classifier227, the example worklist generator 228, the example work ordergenerator 230, the example updater 232, the example tallier 234, theexample filter 236, the example time stamp identifier 238, the exampleweighting factor database 242, and the example central data processingcenter 113.

The processor 912 of the illustrated example includes a local memory 913(e.g., a cache). The processor 912 of the illustrated example is incommunication with a main memory including a volatile memory 914 and anon-volatile memory 916 via a bus 918. The volatile memory 914 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of random access memory device. The non-volatilememory 916 may be implemented by flash memory and/or any other desiredtype of memory device. Access to the main memory 914, 916 is controlledby a memory controller.

The processor platform 900 of the illustrated example also includes aninterface circuit 920. The interface circuit 920 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 922 are connectedto the interface circuit 920. The input devices 922 can be used toimplement the interface 240. The input device(s) 922 permit(s) a user toenter data and/or commands into the processor 912. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 924 are also connected to the interfacecircuit 920 of the illustrated example. The output devices 924 can beused to implement the interface 240. The output devices 924 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 920 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip and/or a graphics driver processor.

The interface circuit 920 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network926 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 900 of the illustrated example also includes oneor more mass storage devices 928 for storing software and/or data.Examples of such mass storage devices 928 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 932 of FIGS. 8, 9 and 10 may be stored in themass storage device 928, in the volatile memory 914, in the non-volatilememory 916, and/or on a removable tangible computer readable storagemedium such as a CD or DVD.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been disclosed that enablework vehicle repairs to be identified and/or prioritized to enable anefficient and/or cost-effective approach to be taken when making suchrepairs. In some examples, repairs of some work vehicles are prioritizedover the repair of other vehicles based on one or more agreements beingin place, feedback from a manufacturer, feedback from an owner/operatorand/or feedback from a servicer (e.g., a dealer). The feedback may beupdatable (e.g., automatically updatable and/or manually updatable) toenable repair prioritizations to change based on one or more factors. Insome examples, the repair prioritizations are based on usage statisticsof the different work vehicles, the usage demand of the different workvehicles based on the time of year, usage demand based on work beingcompleted by the owner/operator, etc.

In some examples, to assist with the repair prioritizations, machinehealth scores may be determined based on one or more alerts generated bya work machine, the frequency of those alerts being generated and/or oneor more weighting factors associated with the work machine and/or thealert. In some examples, the weighting factors are between 0 and 1, butthe weighting factors can be any other number based on the situationand/or the repair. In some examples, the machine health score beinggreater than a threshold indicates that the work vehicle requires moreurgent repair whereas the machine health score satisfying or being lessthan the threshold indicates that the work vehicle does not requireurgent repair (e.g., no maintenance, routine maintenance, etc.). In someexamples, the machine health score may be used to rank work vehiclesrelative to one another. Further, the examples disclosed herein relateto processing diagnostic trouble codes (DTC) to identify a fault on thework vehicle and/or maintenance or a repair to take place on the workvehicle, a component of the work vehicle and/or a vehicle system. Insome examples, using the examples disclosed herein, a servicer cancreate job requests, create repair orders, change the status leveland/or classification of a job and/or alert and/or ignore and/or reporta nuisance alert.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An apparatus, comprising: an interface to accessa first alert from a first work vehicle and a second alert from a secondwork vehicle; an alert scorer to generate a first score for the firstalert and a second score for the second alert, the first and secondscores associated with a severity of the respective alerts; a weightingfactor applier to associate a first weighting factor with the firstalert and to associate a second weighting factor with the second alert;a machine health score determiner to determine a first machine healthscore for the first work vehicle based on the first score and the firstweighting factor and a second machine health score for the second workvehicle based on the second score and the second weighting factor; and aworklist generator to generate a worklist indicating that the secondwork vehicle is to be repaired prior to the first work vehicle based onat least one of the first machine health score or an associated firstclassification and at least one of the second machine health score or anassociated second classification.
 2. The apparatus of claim 1, whereinthe first weighting factor is associated with one or more of a time ofyear, a usage demand of the first work vehicle, or a service agreementassociated with the first work vehicle.
 3. The apparatus of claim 1,wherein one or more of the first score or the first weighting factor isdetermined based on an occurrence count for the first alert and avisibility score for the first alert, the visibility score associatedwith a visibility of the first alert to an operator of the first workvehicle.
 4. The apparatus of claim 1, further including a machine healthscore classifier to determine the first classification based on thefirst machine health score being above an average of machine healthscores for a time period, the machine health score classifier todetermine the second classification based on the second machine healthscore being associated with the average of the machine health scores forthe time period.
 5. The apparatus of claim 1, wherein the machine healthscore determiner is to determine the first machine health score bysumming products of other scores and other weighting factors associatedwith the first work vehicle.
 6. The apparatus of claim 1, wherein theworklist is a first worklist, the machine health score determiner is toupdate the first machine health score in substantially real time basedon third alerts accessed from the first work vehicle and the worklistgenerator is to generate a second worklist indicating that the firstwork vehicle is to be repaired prior to the second work vehicle based onat least one of the updated first machine health score or the associatedclassification.
 7. The apparatus of claim 1, further including: adiagnosis identifier to identify a repair to be performed on the firstwork vehicle based on the first alert; a work order generator togenerate a work order to perform the repair on the first work vehicle; aprobable parts determiner to determine one or more probable parts to beused to repair the first work vehicle; and a probable parts orderer togenerate an order for the one or more probable parts when the one ormore parts are determined as not being available, wherein the worklistgenerator is to indicate the second work vehicle is to be repaired priorto the first work vehicle based on an estimated arrival time of the oneor more parts.
 8. The apparatus of claim 1, further including aresources allocation determiner to determine a first resourcesallocation to repair the first work vehicle based on the first alert anda second resources allocation to repair the second work vehicle based onthe second alert, wherein the worklist generator is to indicate thesecond work vehicle is to be repaired prior to the first work vehiclebased on the first resources allocation and the second resourcesallocation.
 9. The apparatus of claim 1, wherein the first weightingfactor or the second weighting factor is based on one or more ofoperator input, owner input, service input, or manufacturer input.
 10. Atangible computer-readable medium comprising instructions that, whenexecuted, cause a processor to, at least: access a first alert from afirst work vehicle and a second alert from a second work vehicle;generate a first score for the first alert and a second score for thesecond alert, the first and second scores associated with a severity ofthe respective alerts; associate a first weighting factor with the firstalert and to associate a second weighting factor with the second alert;determine a first machine health score for the first work vehicle basedon the first score and the first weighting factor and a second machinehealth score for the second work vehicle based on the second score andthe second weighting factor; and generate a worklist indicating that thesecond work vehicle is to be repaired prior to the first work vehiclebased on at least one of the first machine health score or an associatedfirst classification and at least one of the second machine health scoreor an associated second classification.
 11. The computer-readable mediumas defined in claim 10, wherein the first weighting factor is associatedwith one or more of a time of year, a usage demand of the first workvehicle, or a service agreement associated with the first work vehicle.12. The computer-readable medium as defined in claim 10, wherein theinstructions, when executed, further cause the processor to determinethe first classification based on the first machine health score beingabove an average of machine health scores for a time period, theprocessor to determine the second classification based on the secondmachine health score being associated with the average of the machinehealth scores for the time period.
 13. The computer-readable medium asdefined in claim 10, wherein the first machine health score isdetermined by summing products of other scores and other weightingfactors associated with the first work vehicle.
 14. Thecomputer-readable medium as defined in claim 10, wherein the worklist isa first worklist, wherein the instructions, when executed, further causethe processor to update the first machine health score in substantiallyreal time based on third alerts accessed from the first work vehicle andto generate a second worklist indicating that the first work vehicle isto be repaired prior to the second work vehicle based on at least one ofthe updated first machine health score or the associated classification.15. The computer-readable medium as defined in claim 10, wherein theinstructions, when executed, further cause the processor to: identify arepair to be performed on the first work vehicle based on the firstalert; generate a work order to perform the repair on the first workvehicle; determine one or more probable parts to be used to repair thefirst work vehicle; and generate an order for the one or more probableparts when the one or more parts are determined as not being available,wherein the second work vehicle is to be repaired prior to the firstwork vehicle based on an estimated arrival time of the one or moreparts.
 16. The computer-readable medium as defined in claim 10, whereinthe instructions, when executed, further cause the processor todetermine a first resources allocation to repair the first work vehiclebased on the first alert and a second resources allocation to repair thesecond work vehicle based on the second alert, wherein the second workvehicle is to be repaired prior to the first work vehicle based on thefirst resources allocation and the second resources allocation.
 17. Amethod, comprising: accessing, by executing an instruction with at leastone processor, a first alert from a first work vehicle and a secondalert from a second work vehicle; generating, by executing aninstruction with the at least one processor, a first score for the firstalert and a second score for the second alert, the first and secondscores associated with a severity of the respective alerts; associating,by executing an instruction with the at least one processor, a firstweighting factor with the first alert and to associate a secondweighting factor with the second alert; determining, by executing aninstruction with the at least one processor, a first machine healthscore for the first work vehicle based on the first score and the firstweighting factor and a second machine health score for the second workvehicle based on the second score and the second weighting factor; andgenerating, by executing an instruction with the at least one processor,a worklist indicating that the second work vehicle is to be repairedprior to the first work vehicle based on at least one of the firstmachine health score or an associated first classification and at leastone of the second machine health score or an associated secondclassification.
 18. The method of claim 17, further includingdetermining the first classification based on the first machine healthscore being above an average of machine health scores for a time period,and determining the second classification based on the second machinehealth score being associated with the average of the machine healthscores for the time period.
 19. The method of claim 17, wherein thefirst machine health score is determined by summing products of otherscores and other weighting factors associated with the first workvehicle.
 20. The method of claim 17, wherein the worklist is a firstworklist, further including updating the first machine health score insubstantially real time based on third alerts accessed from the firstwork vehicle and to generate a second worklist indicating that the firstwork vehicle is to be repaired prior to the second work vehicle based onat least one of the updated first machine health score or the associatedclassification.